Dynamic Pricing Déjà vu all over again – or brave new world?

1. Dynamic PricingDéjà vu all over again – or brave new world? Phil Evans !Personal capacity – personal opinions! Senior Consultant Fipra Member – UK Competition Commission

2. What is dynamic pricing? • Dynamic pricing • charging consumers different prices for the same product or service • depending on particular characteristics of the transaction or the consumers. • Consumer characteristics? • Souk – reading people - haggling • Old – location, age, previous purchases • New – any factor with data attached • New location IP tracking to segment markets – e.g. travel, car hire, downloads • BUT need to view with link into: • Personalised pricing • The Souk with information asymmetries! • Algorithmic competition • Stock market trade tech in retail markets

3. Where do we see dynamic pricing? • Travel – airline, train, road tolls • Yield management, peak/off peak pricing • Profiling? Saturday night stay, FFPs, season tickets • Sports – time and profile specific • Season tickets, advance purchase discounts, bundles • Profiling – ‘fans’ – occasional purchasers • See www.qcue.com • Loyalty cards • Coupons, targeted discounts • Sales/discounting/product retail cycles • Launch of new games/products

4. Why does dynamic pricing exist? • ‘Bums on seats’ – maximise per seat revenue for time limited products • Different consumers have different ‘willingness to pay’/price sensitivity • Bank revenue in advance on fixed cost facilities – season tickets • Encourage loyalty and repeat custom: loyalty cards • Maximise profit from individual sales • Effectively catch everyone on the demand curve • Products have a price life cycle – start expensive, then come down in price

5. Déjà vu or brave new world? • From: Déjà vu – quid pro quo markets – dynamic pricing • Travel, sports, retailer loyalty cards • To: Willingness-to-pay markets – dynamic/personalised pricing • Increasing online sophistication • ‘Big Data’ gets personal • Upside • offers for regular purchase items, related items, advance offers, items of interest • Downside • Poor targeting, ‘unfairness’, favoured and unfavoured, regressive pricing, need to game system

6. Examples? • Tesco Clubcard • Personalised coupons based on Clubcard data • Amazon 2000 DVD experiment • Mapped ability to pay by profiling purchase history and residence among other factors. • Displayed different pricing results based on browser used. • Orbitz 2012 • Noticed Mac users spent ave of 30% more on hotel rooms • So displayed higher priced rooms if you use a Mac • Expedia – car rental International Business Times • car rentals in San Francisco between Sept. 1 and Sept. 8 • UK VPN - $311 • US VPN similar search $1,118.

7. The future? ‘Minority Report’ problem ‘Personalised’ advertising triggered by eyeball scanning technology Eyeball scanning patented and being tested More likely ‘general’ personalised advertising using gender, age profiling RFID scanners likely – personal info being read by scanners linked to advertising May not work! the ‘racist camera’ scanning mistaking men for women etc poor targeting – buy cough sweets get offered pregnancy test (personal case: eBay) Yves Rocher – convinced I am a woman – ‘you too are a Queen’! Google – convinced I need a discrete male catheter (sports/age profiling?) Consumer acceptance the mildly embarrassing – haemorrhoid cream the really embarrassing - see Target right Forbes: 16/2/2012: How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did

8. Brave new world? • Dynamic+personalised pricing changes things • ‘Fair’ trade off markets based on quid-pro-quo • ‘Unfair’ targeted markets based on ‘revealed consumer WTP+information asymmetry’ • Bit like visiting the Souk and every trader knowing exactly what you have bought in the past, how much you paid, what you liked/disliked, the names of all your kids, friends, favourite bands – while you know nothing about them • Dynamic/personalised pricing meet algorithmic competition • Competition requires consumers to notice price cutting by retailer A which triggers retailer B • BUT if A and B use algorithms to track discounting – consumer cannot reward A and so incentive to cut prices generally disappears

9. Implications • Price discrimination needs • Market power (does info in DP/PP give every seller power?) • Understanding of consumer reference pricing (definitely) • Ability to stop arbitrage (no –other vertical restraints can) • Dynamic pricing • Everyone can generally access the different pricing • Dynamic/personalised pricing • Everyone gets a different price at different times • Built on asymmetric information • Built on untransparent personal data and modelling • Unequal access and not necessary to have quid-pro-quo

10. Conclusions • Dynamic pricing • Common, quid-pro-quo; consumers ‘learn’/predict most markets • Dynamic/personalised pricing • Experiments 10 yrs+ BUT Big Data facilitates greater use • Asymmetric info undermines quid-pro-quo of DP • Dynamic/personalised pricing + algorithmic competition? • Price discrimination to the nth degree? • No anonymous behaviour/shopping/transparency/consumer learning/reference pricing • Two fundamental problems to address: • Whose data is it anyway? • Someone else is monetising my personal data can I license it/sell it • Is this ‘fair’? • Fairness in consumer transactions important legal and societal issue